code: def load_item(self, sample):
      text = str(sample["text"])
      tseq = self.get_text(text, sample["language"])
      audiopath = sample["audio_file"]
      wav = load_audio(audiopath, self.sample_rate)
      if text is None or len(text.strip()) == 0:
          raise ValueError
      if wav is None or wav.shape[-1] < (0.5 * self.sample_rate):
          # Ultra short clips are also useless (and can cause problems within some models).
          raise ValueError
  
      if self.use_masking_gt_prompt_approach:
          # get a slice from GT to condition the model
          cond, _, cond_idxs = get_prompt_slice(
              audiopath,
              self.max_conditioning_length,
              self.min_conditioning_length,
              self.sample_rate,
              self.is_eval,
          )
          # if use masking do not use cond_len
          cond_len = torch.nan
      else:
          ref_sample = (
              sample["reference_path"]
              if "reference_path" in sample
              and sample["reference_path"] is not None
              else audiopath
          )
          cond, cond_len, _ = get_prompt_slice(
              ref_sample,
              self.max_conditioning_length,
              self.min_conditioning_length,
              self.sample_rate,
              self.is_eval,
          )
          # if do not use masking use cond_len
          cond_idxs = torch.nan
  
      return tseq, audiopath, wav, cond, cond_len, cond_idxs
  
file_path: TTS/TTS/tts/layers/xtts/trainer/dataset.py
imports: import os
  import random
  import sys
  import torch
  import torch.nn.functional
  import torch.utils.data
  from TTS.tts.models.xtts import load_audio
label_desc: Use image node_id 4 for calling the XTTSDataset obj's underlying member method code with example usage: obj.load_item(sample) and returns: tseq,  audiopath,  wav,  cond,  cond_len,  cond_idxs